class_docstr: Give a sentiment intensity score to sentences.
docstr: Return a float for sentiment strength based on the input text.
  Positive values are positive valence, negative value are negative
  valence.
  
  :note: Hashtags are not taken into consideration (e.g. #BAD is neutral). If you
      are interested in processing the text in the hashtags too, then we recommend
      preprocessing your data to remove the #, after which the hashtag text may be
      matched as if it was a normal word in the sentence.
docstr_tok: ["Return","a","float","for","sentiment","strength","based","on","the","input","text",".","Positive","values","are","positive","valence",",","negative","value","are","negative","valence",".",":","note",":","Hashtags","are","not","taken","into","consideration","(","e.g",".","#","BAD","is","neutral",")",".","If","you","are","interested","in","processing","the","text","in","the","hashtags","too",",","then","we","recommend","preprocessing","your","data","to","remove","the","#",",","after","which","the","hashtag","text","may","be","matched","as","if","it","was","a","normal","word","in","the","sentence","."]
code: def polarity_scores(self, text):
      """
      Return a float for sentiment strength based on the input text.
      Positive values are positive valence, negative value are negative
      valence.
  
      :note: Hashtags are not taken into consideration (e.g. #BAD is neutral). If you
          are interested in processing the text in the hashtags too, then we recommend
          preprocessing your data to remove the #, after which the hashtag text may be
          matched as if it was a normal word in the sentence.
      """
      # text, words_and_emoticons, is_cap_diff = self.preprocess(text)
      sentitext = SentiText(
          text,
          self.constants.PUNC_LIST,
          self.constants.REGEX_REMOVE_PUNCTUATION,
      )
      sentiments = []
      words_and_emoticons = sentitext.words_and_emoticons
      for item in words_and_emoticons:
          valence = 0
          i = words_and_emoticons.index(item)
          if (
              i < len(words_and_emoticons) - 1
              and item.lower() == "kind"
              and words_and_emoticons[i + 1].lower() == "of"
          ) or item.lower() in self.constants.BOOSTER_DICT:
              sentiments.append(valence)
              continue
  
          sentiments = self.sentiment_valence(
              valence, sentitext, item, i, sentiments
          )
  
      sentiments = self._but_check(words_and_emoticons, sentiments)
  
      return self.score_valence(sentiments, text)
  
imports: import math
  import re
  import string
  from itertools import product
  import nltk.data
  from nltk.util import pairwise
label_desc: Use image node_id 3 for calling the SentimentIntensityAnalyzer obj's underlying member method code with example usage: obj.polarity_scores(text) and returns: self